The relationship between school physical activity policy environment and physical literacy among primary school students: Evidence from Henan Province, China
Bibliographic record
Abstract
BACKGROUND: Chinese primary school students spend the majority of their day at school, making the school physical activity policy environment (SPAPE) crucial to their development of physical literacy (PL). However, research exploring the relationship between SPAPE and PL remains limited. This study aims to investigate the association between SPAPE and students' PL levels. METHODS: A total of 408 primary school students (206 boys and 202 girls) were included in the data analysis. The School Physical Activity Environment Questionnaire (SPAEQ) and the Canadian Assessment of Physical Literacy-Edition 2 (CAPL-2) were used to assess the policy environment and PL levels, respectively. Pearson correlation coefficients were calculated to explore the relationship between the policy environment and PL. Additionally, ANOVA and MANOVA analyses were conducted to examine the effects of age, gender, and their interaction on the relationship. RESULTS: A significant positive correlation was found between SPAPE and PL, with boys (r = 0.59, p < 0.01) and girls (r = 0.48, p < 0.01) both showing moderate to strong associations. MANOVA results revealed significant gender differences for Daily Behavior (DB) (F (1, 406) = 14.24, p < 0.01, partial η² = .04) and Motivation and Confidence (MC) (F (1, 406) = 4.72, p < 0.05, partial η² = .01). Significant age differences were observed for MC (F (4, 403) = 5.68, p < 0.01, partial η² = .05) and Knowledge and Understanding (KU) (F (4, 403) = 8.57, p < 0.01, partial η² = .08). No significant effects of age, gender, or interaction were found in relation to SPAPE. CONCLUSION: This study is the first to explore the relationship between PL and SPAPE in Chinese primary school students. The results highlight the significant association between SPAPE and PL, with notable gender and age differences. These findings emphasize the importance of tailoring PA policies to account for demographic factors to effectively promote PL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".